# --------------------------------------------------------
# InternVL
# Copyright (c) 2024 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------

from typing import Optional, Tuple, Union

import math
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from einops import rearrange
from timm.models.layers import DropPath
from torch import nn
from transformers.activations import ACT2FN
from transformers.modeling_outputs import (BaseModelOutput,
                                           BaseModelOutputWithPooling)
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging

from .configuration_intern_vit import InternVisionConfig

try:
    from flash_attn.bert_padding import pad_input, unpad_input
    from flash_attn.flash_attn_interface import \
        flash_attn_varlen_qkvpacked_func, flash_attn_varlen_func
    has_flash_attn = True
except:
    print('FlashAttention2 is not installed.')
    has_flash_attn = False

logger = logging.get_logger(__name__)


class FlashAttention(nn.Module):
    """Implement the scaled dot product attention with softmax.
    Arguments
    ---------
        softmax_scale: The temperature to use for the softmax attention.
                      (default: 1/sqrt(d_keys) where d_keys is computed at
                      runtime)
        attention_dropout: The dropout rate to apply to the attention
                           (default: 0.0)
    """

    def __init__(self, softmax_scale=None, attention_dropout=0.0, device=None, dtype=None):
        super().__init__()
        self.softmax_scale = softmax_scale
        self.dropout_p = attention_dropout

    def forward(self, qkv, key_padding_mask=None, causal=False, cu_seqlens=None,
                max_s=None, need_weights=False):
        """Implements the multihead softmax attention.
        Arguments
        ---------
            qkv: The tensor containing the query, key, and value. (B, S, 3, H, D) if key_padding_mask is None
                if unpadded: (nnz, 3, h, d)
            key_padding_mask: a bool tensor of shape (B, S)
        """
        assert not need_weights
        assert qkv.dtype in [torch.float16, torch.bfloat16]
        assert qkv.is_cuda

        if cu_seqlens is None:
            batch_size = qkv.shape[0]
            seqlen = qkv.shape[1]
            if key_padding_mask is None:
                qkv = rearrange(qkv, 'b s ... -> (b s) ...')
                max_s = seqlen
                cu_seqlens = torch.arange(0, (batch_size + 1) * seqlen, step=seqlen, dtype=torch.int32,
                                          device=qkv.device)
                output = flash_attn_varlen_qkvpacked_func(
                    qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
                    softmax_scale=self.softmax_scale, causal=causal
                )
                output = rearrange(output, '(b s) ... -> b s ...', b=batch_size)
            else:
                nheads = qkv.shape[-2]
                x = rearrange(qkv, 'b s three h d -> b s (three h d)')
                x_unpad, indices, cu_seqlens, max_s = unpad_input(x, key_padding_mask)
                x_unpad = rearrange(x_unpad, 'nnz (three h d) -> nnz three h d', three=3, h=nheads)
                output_unpad = flash_attn_varlen_qkvpacked_func(
                    x_unpad, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
                    softmax_scale=self.softmax_scale, causal=causal
                )
                output = rearrange(pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'),
                                             indices, batch_size, seqlen),
                                   'b s (h d) -> b s h d', h=nheads)
        else:
            assert max_s is not None
            output = flash_attn_varlen_qkvpacked_func(
                qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
                softmax_scale=self.softmax_scale, causal=causal
            )

        return output, None


class InternRMSNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward(self, hidden_states):
        input_dtype = hidden_states.dtype
        hidden_states = hidden_states.to(torch.float32)
        variance = hidden_states.pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
        return self.weight * hidden_states.to(input_dtype)


try:
    from apex.normalization import FusedRMSNorm

    InternRMSNorm = FusedRMSNorm  # noqa

    logger.info('Discovered apex.normalization.FusedRMSNorm - will use it instead of InternRMSNorm')
except ImportError:
    # using the normal InternRMSNorm
    pass
except Exception:
    logger.warning('discovered apex but it failed to load, falling back to InternRMSNorm')
    pass


NORM2FN = {
    'rms_norm': InternRMSNorm,
    'layer_norm': nn.LayerNorm,
}


class InternVisionEmbeddings(nn.Module):
    def __init__(self, config: InternVisionConfig):
        super().__init__()
        self.config = config
        self.embed_dim = config.hidden_size
        self.image_size = config.image_size
        self.patch_size = config.patch_size

        self.class_embedding = nn.Parameter(
            torch.randn(1, 1, self.embed_dim),
        )

        self.patch_embedding = nn.Conv2d(
            in_channels=3, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size
        )

        self.num_patches = (self.image_size // self.patch_size) ** 2
        self.num_positions = self.num_patches + 1

        self.position_embedding = nn.Parameter(torch.randn(1, self.num_positions, self.embed_dim))

    def _get_pos_embed(self, pos_embed, H, W):
        target_dtype = pos_embed.dtype
        pos_embed = pos_embed.float().reshape(
            1, self.image_size // self.patch_size, self.image_size // self.patch_size, -1).permute(0, 3, 1, 2)
        pos_embed = F.interpolate(pos_embed, size=(H, W), mode='bicubic', align_corners=False). \
            reshape(1, -1, H * W).permute(0, 2, 1).to(target_dtype)
        return pos_embed

    def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
        target_dtype = self.patch_embedding.weight.dtype
        patch_embeds = self.patch_embedding(pixel_values)  # shape = [*, channel, width, height]
        batch_size, _, height, width = patch_embeds.shape
        patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
        class_embeds = self.class_embedding.expand(batch_size, 1, -1).to(target_dtype)
        embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
        position_embedding = torch.cat([
            self.position_embedding[:, :1, :],
            self._get_pos_embed(self.position_embedding[:, 1:, :], height, width)
        ], dim=1)
        embeddings = embeddings + position_embedding.to(target_dtype)
        return embeddings


class InternAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(self, config: InternVisionConfig):
        super().__init__()
        self.config = config
        self.embed_dim = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.use_flash_attn = config.use_flash_attn and has_flash_attn
        if config.use_flash_attn and not has_flash_attn:
            print('Warning: Flash Attention is not available, use_flash_attn is set to False.')
        self.head_dim = self.embed_dim // self.num_heads
        if self.head_dim * self.num_heads != self.embed_dim:
            raise ValueError(
                f'embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:'
                f' {self.num_heads}).'
            )

        self.scale = self.head_dim ** -0.5
        self.qkv = nn.Linear(self.embed_dim, 3 * self.embed_dim, bias=config.qkv_bias)
        self.attn_drop = nn.Dropout(config.attention_dropout)
        self.proj_drop = nn.Dropout(config.dropout)

        self.qk_normalization = config.qk_normalization

        if self.qk_normalization:
            self.q_norm = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
            self.k_norm = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)

        if self.use_flash_attn:
            self.inner_attn = FlashAttention(attention_dropout=config.attention_dropout)
        self.proj = nn.Linear(self.embed_dim, self.embed_dim)

    def _naive_attn(self, x):
        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv.unbind(0)  # make torchscript happy (cannot use tensor as tuple)

        if self.qk_normalization:
            B_, H_, N_, D_ = q.shape
            q = self.q_norm(q.transpose(1, 2).flatten(-2, -1)).view(B_, N_, H_, D_).transpose(1, 2)
            k = self.k_norm(k.transpose(1, 2).flatten(-2, -1)).view(B_, N_, H_, D_).transpose(1, 2)

        attn = ((q * self.scale) @ k.transpose(-2, -1))
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x

    def _flash_attn(self, x, key_padding_mask=None, need_weights=False):
        qkv = self.qkv(x)
        qkv = rearrange(qkv, 'b s (three h d) -> b s three h d', three=3, h=self.num_heads)

        if self.qk_normalization:
            q, k, v = qkv.unbind(2)
            q = self.q_norm(q.flatten(-2, -1)).view(q.shape)
            k = self.k_norm(k.flatten(-2, -1)).view(k.shape)
            qkv = torch.stack([q, k, v], dim=2)

        context, _ = self.inner_attn(
            qkv, key_padding_mask=key_padding_mask, need_weights=need_weights, causal=False
        )
        outs = self.proj(rearrange(context, 'b s h d -> b s (h d)'))
        outs = self.proj_drop(outs)
        return outs

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        x = self._naive_attn(hidden_states) if not self.use_flash_attn else self._flash_attn(hidden_states)
        return x


class InternMLP(nn.Module):
    def __init__(self, config: InternVisionConfig):
        super().__init__()
        self.config = config
        self.act = ACT2FN[config.hidden_act]
        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.fc1(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states = self.fc2(hidden_states)
        return hidden_states


def generate_batch_temporal_mask(split_sizes, device='cpu'):
    """
    generate the temporal (padding) mask of a batch
    Args:
        split_sizes: List[num frames]
    Returns:
        temporal_mask: BoolTensor(B, T), `True` means taking, `False` means padding
    """
    B, T = len(split_sizes), max(split_sizes)
    split_sizes = torch.tensor(split_sizes, dtype=torch.long, device=device)
    temporal_idx = torch.arange(T, dtype=torch.long, device=device)[None].repeat((B, 1))
    temporal_mask = temporal_idx < split_sizes[:, None]
    return temporal_mask

def concat_batch_frames(images, split_sizes=None, temporal_mask=None):
    """
    B, T, L, D -> concat(T), L, D
    """
    if temporal_mask is None:
        assert split_sizes is not None
        temporal_mask = generate_batch_temporal_mask(split_sizes, device=images.device)
    return images[temporal_mask]

def stack_batch_frames(images, split_sizes, return_mask=False):
    """
    concat(T), L, D -> B, T, L, D
    """
    B, T = len(split_sizes), max(split_sizes)
    images_stack = images.new_zeros((B, T, *images.shape[1:]))
    temporal_mask = generate_batch_temporal_mask(split_sizes, device=images.device)
    images_stack[temporal_mask] = images
    if return_mask:
        return images_stack, temporal_mask
    return images_stack

def temporal_idx_abs_to_rel(temporal_idx, split_sizes):
    stacked_temporal_idx = stack_batch_frames(temporal_idx, split_sizes)
    length = stacked_temporal_idx.max(dim=-1, keepdim=True)[0]
    length = length.clip(min=1)
    rel_temporal_idx = stacked_temporal_idx.float() / length.float()
    rel_temporal_idx = concat_batch_frames(rel_temporal_idx, split_sizes)
    return rel_temporal_idx


def get_timestep_embedding(
    timesteps: torch.Tensor,
    embedding_dim: int,
    flip_sin_to_cos: bool = False,
    downscale_freq_shift: float = 1,
    scale: float = 1,
    max_period: int = 10000,
):
    """
    This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.

    Args
        timesteps (torch.Tensor):
            a 1-D Tensor of N indices, one per batch element. These may be fractional.
        embedding_dim (int):
            the dimension of the output.
        flip_sin_to_cos (bool):
            Whether the embedding order should be `cos, sin` (if True) or `sin, cos` (if False)
        downscale_freq_shift (float):
            Controls the delta between frequencies between dimensions
        scale (float):
            Scaling factor applied to the embeddings.
        max_period (int):
            Controls the maximum frequency of the embeddings
    Returns
        torch.Tensor: an [N x dim] Tensor of positional embeddings.
    """
    assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"
    original_dtype = timesteps.dtype

    half_dim = embedding_dim // 2
    exponent = -math.log(max_period) * torch.arange(
        start=0, end=half_dim, dtype=torch.float32, device=timesteps.device
    )
    exponent = exponent / (half_dim - downscale_freq_shift)

    emb = torch.exp(exponent)
    emb = timesteps[:, None].float() * emb[None, :]

    # scale embeddings
    emb = scale * emb

    # concat sine and cosine embeddings
    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)

    # flip sine and cosine embeddings
    if flip_sin_to_cos:
        emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)

    # zero pad
    if embedding_dim % 2 == 1:
        emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
    return emb.to(original_dtype)

class Timesteps(nn.Module):
    def __init__(self, num_channels: int, flip_sin_to_cos: bool = False, downscale_freq_shift: float = 0, scale: int = 1):
        super().__init__()
        self.num_channels = num_channels
        self.flip_sin_to_cos = flip_sin_to_cos
        self.downscale_freq_shift = downscale_freq_shift
        self.scale = scale

    def forward(self, timesteps):
        t_emb = get_timestep_embedding(
            timesteps,
            self.num_channels,
            flip_sin_to_cos=self.flip_sin_to_cos,
            downscale_freq_shift=self.downscale_freq_shift,
            scale=self.scale,
        )
        return t_emb

class AdaLayerNorm(nn.Module):
    def __init__(
        self,
        embedding_dim: int,
        conditioning_embedding_dim: int,
        elementwise_affine=False,
        eps=1e-5,
        bias=True,
        norm_type="layer_norm",
        zero_init=False,
    ):
        super().__init__()
        self.silu = nn.SiLU()
        self.linear = nn.Linear(conditioning_embedding_dim, embedding_dim * 2, bias=bias)
        if zero_init:
            nn.init.zeros_(self.linear.weight)
            nn.init.zeros_(self.linear.bias)
            print('AdaLN zero init')
        if norm_type == "layer_norm":
            self.norm = nn.LayerNorm(embedding_dim, eps, elementwise_affine, bias)
        else:
            raise ValueError(f"unknown norm_type {norm_type}")

    def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tensor) -> torch.Tensor:
        emb = self.linear(self.silu(conditioning_embedding).to(x.dtype))
        scale, shift = torch.chunk(emb, 2, dim=-1)
        x = self.norm(x) * (1 + scale) + shift
        return x


class TokenTemporalAttention(nn.Module):
    def __init__(self, config: InternVisionConfig):
        super().__init__()
        self.config = config

        d_model = config.hidden_size
        temporal_num_heads = config.num_attention_heads
        self.temporal_attn = nn.MultiheadAttention(d_model, temporal_num_heads, batch_first=True)

        self.timestep_scale = self.config.relative_timestep_scale
        self.time_embed = nn.Sequential(
            Timesteps(num_channels=256),
            nn.Linear(256, d_model),
            nn.SiLU(),
            nn.Linear(d_model, d_model),
        )
        self.adaln = AdaLayerNorm(d_model, d_model, eps=config.layer_norm_eps,
                                    zero_init=self.config.temporal_adaln_zero_init)
        if self.config.temporal_adaln_hidden_condition:
            self.hidden_condition_proj = nn.Sequential(
                nn.Linear(d_model, d_model),
                nn.SiLU(), # default use `SiLU`
                nn.Linear(d_model, d_model)
            )
        
        if self.config.temporal_alpha_channelwise:
            self.alpha_xattn = nn.Parameter(self.config.temporal_alpha_init * torch.ones(d_model),
                                            requires_grad=True)
        else:
            self.alpha_xattn = nn.Parameter(torch.tensor(self.config.temporal_alpha_init), requires_grad=True)

    def forward(self, 
        hidden_states: torch.Tensor,
        split_sizes: Optional[list] = None,
        place: Optional[str] = None,
        temporal_id: Optional[torch.LongTensor] = None,
    ):
        # use flash attention 2
        if self.config.use_flash_attn:
            return self._forward_flash_attention_2(hidden_states, split_sizes, place, temporal_id)

        # stack temporal dim
        hidden_states = stack_batch_frames(hidden_states, split_sizes) # concat(T) L D -> B T L D
        residual = hidden_states
        B, T, L, D = hidden_states.shape
        x = hidden_states.transpose(1, 2).flatten(0, 1) # B T L D -> B*L, T, D

        # attn & padding mask
        temporal_mask = generate_batch_temporal_mask(split_sizes, device=hidden_states.device) # (B, T), 0 indicate masked out
        temporal_mask = temporal_mask.unsqueeze(1).expand(B, L, T).flatten(0, 1) # B T -> B L T -> B*L, T
        if self.config.temporal_causal:
            attn_mask = torch.ones(T, T, dtype=torch.bool, device=hidden_states.device).tril(diagonal=0) # (T, T), 0 indicate masked out
        else:
            attn_mask = None

        # temporal AdaLN
        timestep = temporal_idx_abs_to_rel(temporal_id, split_sizes)
        timestep = timestep * self.timestep_scale
        time_condition = self.time_embed(timestep.to(hidden_states.dtype)) # N D
        time_condition = stack_batch_frames(time_condition, split_sizes) # N D -> B T D
        time_condition = time_condition.unsqueeze(1).repeat(1, L, 1, 1).flatten(0, 1) # B T D -> B L T D -> B*L, T, D
        condition = time_condition
        if self.config.temporal_adaln_hidden_condition:
            condition = condition + self.hidden_condition_proj(x)
        x = self.adaln(x, condition)

        # pass attention
        q = k = v = x
        attn_mask = ~attn_mask if attn_mask is not None else None
        temporal_mask = ~temporal_mask
        # attn_mask, temporal_mask = ~attn_mask, ~temporal_mask, MHSA use 1 to indicate masked out
        attn_out = self.temporal_attn(q, k, v, attn_mask=attn_mask, key_padding_mask=temporal_mask)
        x = attn_out[0]

        # add to residual
        x = x.view(B, L, T, D).transpose(1, 2) # B*L, T, D -> B T L D
        hidden_states = residual + x * self.alpha_xattn

        # concat temporal dim
        hidden_states = concat_batch_frames(hidden_states, split_sizes) # B T L D -> concat(T) L D

        return hidden_states

    def _forward_flash_attention_2(self, 
        hidden_states: torch.Tensor,
        split_sizes: Optional[list] = None,
        place: Optional[str] = None,
        temporal_id: Optional[torch.LongTensor] = None,
    ):
        B, T = len(split_sizes), max(split_sizes)
        N, L, D = hidden_states.shape
        residual = hidden_states
        hidden_states = hidden_states.transpose(0, 1).flatten(0, 1) # (N, L, D) -> (L, N, D) -> (L*N, D)
        
        # temporal AdaLN
        timestep = temporal_idx_abs_to_rel(temporal_id, split_sizes)
        timestep = timestep * self.timestep_scale
        time_condition = self.time_embed(timestep.to(hidden_states.dtype)) # (N, D)
        time_condition = time_condition.unsqueeze(0).repeat(L, 1, 1).flatten(0, 1) # (L*N, D)
        condition = time_condition
        if self.config.temporal_adaln_hidden_condition:
            condition = condition + self.hidden_condition_proj(hidden_states)
        hidden_states = self.adaln(hidden_states, condition)

        q = k = v = hidden_states # (L*N, D)
        w_q, w_k, w_v = self.temporal_attn.in_proj_weight.chunk(3)
        b_q, b_k, b_v = self.temporal_attn.in_proj_bias.chunk(3)
        q = F.linear(q, w_q, b_q)
        k = F.linear(k, w_k, b_k)
        v = F.linear(v, w_v, b_v)

        num_heads, head_dim = self.temporal_attn.num_heads, self.temporal_attn.head_dim
        q = q.view(q.shape[0], num_heads, head_dim)
        k = k.view(k.shape[0], num_heads, head_dim)
        v = v.view(v.shape[0], num_heads, head_dim)

        cu_len = torch.cumsum(torch.tensor(split_sizes, dtype=torch.int, device=hidden_states.device), dim=0)
        cu_lens = [cu_len + i * N for i in range(L)]
        cu_lens = torch.cat([torch.zeros((1, ), device=hidden_states.device)] + cu_lens).to(torch.int)
        max_len = max(split_sizes)

        out = flash_attn_varlen_func(
            q=q, k=k, v=v, 
            cu_seqlens_q=cu_lens, 
            cu_seqlens_k=cu_lens, 
            max_seqlen_q=max_len, 
            max_seqlen_k=max_len,
            causal=self.config.temporal_causal,
        )

        out = out.view(q.shape[0], num_heads*head_dim)
        out = self.temporal_attn.out_proj(out) # (L*N, D)
        out = out.view(L, N, D).transpose(0, 1).contiguous() # (L*N, D) -> (L, N, D) -> (N, L, D)

        # add to residual
        hidden_states = residual + out * self.alpha_xattn
        return hidden_states


class InternVisionTemporalEncoderLayer(nn.Module):
    def __init__(self, config: InternVisionConfig, drop_path_rate: float, layer_idx: int=None):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        self.embed_dim = config.hidden_size
        self.intermediate_size = config.intermediate_size
        self.norm_type = config.norm_type

        self.attn = InternAttention(config)
        self.mlp = InternMLP(config)
        self.norm1 = NORM2FN[self.norm_type](self.embed_dim, eps=config.layer_norm_eps)
        self.norm2 = NORM2FN[self.norm_type](self.embed_dim, eps=config.layer_norm_eps)

        self.ls1 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
        self.ls2 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
        self.drop_path1 = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
        self.drop_path2 = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()

    def initialize_temporal_module(self):
        temporal_layer_ids = self.config.temporal_layer_ids
        if (temporal_layer_ids is not None) and self.layer_idx not in temporal_layer_ids:
            self.temporal_module = None
            return

        self.temporal_module = TokenTemporalAttention(self.config)
        self.temporal_module_place = self.config.temporal_module_place
        param_names = [k for k, v in self.temporal_module.named_parameters()]
        print(f"[vision temporal model] layer {self.layer_idx} initialize temporal module. "
              f"Place: {self.temporal_module_place}. Parameters: {param_names}")

    def forward(
            self,
            hidden_states: torch.Tensor,
            split_sizes: Optional[list] = None,
            temporal_id: Optional[torch.LongTensor] = None,
    ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor], Optional[Tuple[torch.FloatTensor]]]:
        """
        Args:
            hidden_states (`Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]`): input to the layer of shape `(batch, seq_len, embed_dim)`
        """
        if (self.temporal_module is not None) and ('before_self_attn' in self.temporal_module_place):
            hidden_states = self.temporal_module(hidden_states, split_sizes, temporal_id=temporal_id, place='before_self_attn')

        hidden_states = hidden_states + self.drop_path1(self.attn(self.norm1(hidden_states).to(hidden_states.dtype)) * self.ls1)

        # default: pass temporal module (between self-attn and MLP)
        if (self.temporal_module is not None) and ('after_self_attn' in self.temporal_module_place):
            hidden_states = self.temporal_module(hidden_states, split_sizes, temporal_id=temporal_id, place='after_self_attn')

        hidden_states = hidden_states + self.drop_path2(self.mlp(self.norm2(hidden_states).to(hidden_states.dtype)) * self.ls2)

        if (self.temporal_module is not None) and ('after_mlp' in self.temporal_module_place):
            hidden_states = self.temporal_module(hidden_states, split_sizes, temporal_id=temporal_id, place='after_mlp')

        return hidden_states


class InternVisionTemporalEncoder(nn.Module):
    """
    Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
    [`InternEncoderLayer`].

    Args:
        config (`InternConfig`):
            The corresponding vision configuration for the `InternEncoder`.
    """

    def __init__(self, config: InternVisionConfig):
        super().__init__()
        self.config = config
        # stochastic depth decay rule
        dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, config.num_hidden_layers)]
        self.layers = nn.ModuleList([
            InternVisionTemporalEncoderLayer(config, dpr[idx], layer_idx=idx)
            for idx in range(config.num_hidden_layers)
        ])
        self.gradient_checkpointing = True

    def forward(
            self,
            inputs_embeds,
            output_hidden_states: Optional[bool] = None,
            return_dict: Optional[bool] = None,
            split_sizes: Optional[list] = None,
            temporal_id: Optional[torch.LongTensor] = None,
    ) -> Union[Tuple, BaseModelOutput]:
        r"""
        Args:
            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
                Embedded representation of the inputs. Should be float, not int tokens.
            output_hidden_states (`bool`, *optional*):
                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                for more detail.
            return_dict (`bool`, *optional*):
                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
        """
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        encoder_states = () if output_hidden_states else None
        hidden_states = inputs_embeds

        for idx, encoder_layer in enumerate(self.layers):
            if output_hidden_states:
                encoder_states = encoder_states + (hidden_states,)
            if self.gradient_checkpointing and self.training:
                layer_outputs = torch.utils.checkpoint.checkpoint(
                    encoder_layer,
                    hidden_states,
                    split_sizes,
                    temporal_id)
            else:
                layer_outputs = encoder_layer(
                    hidden_states,
                    split_sizes=split_sizes,
                    temporal_id=temporal_id,
                )
            hidden_states = layer_outputs

        if output_hidden_states:
            encoder_states = encoder_states + (hidden_states,)

        if not return_dict:
            return tuple(v for v in [hidden_states, encoder_states] if v is not None)
        return BaseModelOutput(
            last_hidden_state=hidden_states, hidden_states=encoder_states
        )


class InternVisionTemporalModel(PreTrainedModel):
    main_input_name = 'pixel_values'
    _supports_flash_attn_2 = True
    config_class = InternVisionConfig
    _no_split_modules = ['InternVisionTemporalEncoderLayer']

    def __init__(self, config: InternVisionConfig, delay_init_new_param=False):
        super().__init__(config)
        self.config = config

        self.embeddings = InternVisionEmbeddings(config)
        self.encoder = InternVisionTemporalEncoder(config)

        self.new_param_inited = False
        if delay_init_new_param:
            print(f"[vision temporal model] delay_init_new_param={delay_init_new_param}, temporal module should be initalized later")
        else:
            print(f"[vision temporal model] delay_init_new_param={delay_init_new_param}")
            self.initialize_temporal_module()
    
    def initialize_temporal_module(self):
        if self.new_param_inited:
            print("[vision temporal model] Warning!!! temporal modules have been initialized, skip.")
            return
        print("[vision temporal model] Initializing temporal modules...")
        for layer in self.encoder.layers:
            layer.initialize_temporal_module()
        self.new_param_inited = True

    def resize_pos_embeddings(self, old_size, new_size, patch_size):
        pos_emb = self.embeddings.position_embedding
        _, num_positions, embed_dim = pos_emb.shape
        cls_emb = pos_emb[:, :1, :]
        pos_emb = pos_emb[:, 1:, :].reshape(1, old_size // patch_size, old_size // patch_size, -1).permute(0, 3, 1, 2)
        pos_emb = F.interpolate(pos_emb.float(), size=new_size // patch_size, mode='bicubic', align_corners=False)
        pos_emb = pos_emb.to(cls_emb.dtype).reshape(1, embed_dim, -1).permute(0, 2, 1)
        pos_emb = torch.cat([cls_emb, pos_emb], dim=1)
        self.embeddings.position_embedding = nn.Parameter(pos_emb)
        self.embeddings.image_size = new_size
        logger.info('Resized position embeddings from {} to {}'.format(old_size, new_size))

    def get_input_embeddings(self):
        return self.embeddings

    def forward(
            self,
            pixel_values: Optional[torch.FloatTensor] = None,
            output_hidden_states: Optional[bool] = None,
            return_dict: Optional[bool] = None,
            pixel_embeds: Optional[torch.FloatTensor] = None,
            split_sizes: Optional[list] = None,
            temporal_id: Optional[torch.LongTensor] = None,
    ) -> Union[Tuple, BaseModelOutputWithPooling]:
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if pixel_values is None and pixel_embeds is None:
            raise ValueError('You have to specify pixel_values or pixel_embeds')

        if pixel_embeds is not None:
            hidden_states = pixel_embeds
        else:
            if len(pixel_values.shape) == 4:
                hidden_states = self.embeddings(pixel_values)
            else:
                raise ValueError(f'wrong pixel_values size: {pixel_values.shape}')
        encoder_outputs = self.encoder(
            inputs_embeds=hidden_states,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            split_sizes=split_sizes,
            temporal_id=temporal_id,
        )
        last_hidden_state = encoder_outputs.last_hidden_state
        pooled_output = last_hidden_state[:, 0, :]

        if not return_dict:
            return (last_hidden_state, pooled_output) + encoder_outputs[1:]

        return BaseModelOutputWithPooling(
            last_hidden_state=last_hidden_state,
            pooler_output=pooled_output,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )